Vehicle sound analysis

ABSTRACT

The disclosure generally relates to collecting and analyzing audible signals produced by a vehicle.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application claims priority to and the benefit ofprovisional patent application 62/367,937 filed Jul. 28, 2016 which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

Aspects herein generally relate to collecting and analyzing sound, andmore particularly relate to identifying vehicles based on capturedportions of sound.

SUMMARY

In an embodiment, a method comprises detecting a new audio segmentassociated with a vehicle and comparing the new audio segment with knownaudio segments in a vehicle audio database. If a closest match can befound, the method comprises identifying a closest match to the new audiosegment from the known audio segments. If a closest match cannot befound, the method comprises creating a new entry in the vehicle audiodatabase.

In an embodiment, a system comprises a vehicle audio database configuredto store vehicle audio segments and metadata associated with the vehicleaudio segments, a communication module for receiving new audio segmentsand new metadata from a sensor, and an analysis module configured toanalyze the new audio segments and the new metadata in relation to thevehicle audio segments and the metadata.

Additional and alternative aspects will be apparent on review of otherportions of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

So that those having ordinary skill in the art, to which the presentinvention pertains, will more readily understand how to employ the novelsystem and methods of the present invention, certain illustratedembodiments thereof will be described in detail herein-below withreference to the drawings, wherein:

FIG. 1 is a flowchart of a methodology depicting illustrative operationof one or more embodiments described herein;

FIG. 2 is a flowchart of a methodology depicting illustrative operationof one or more embodiments described herein;

FIG. 3 is a flowchart of a methodology depicting illustrative operationof one or more embodiments described herein;

FIG. 4 depicts an example computing device as might be practiced withthe one or more embodiments described herein; and

FIG. 5 illustrates a block diagram of an example system disclosedherein.

A component or a feature that is common to more than one drawing isindicated with the same reference number in each of the drawings.

DETAILED DESCRIPTION

The present disclosure is generally directed to collection and analysisof audio signals related to vehicles.

Many motor sports enthusiasts enjoy and recognize the sounds generatedby vehicle engines and other components. Some go so far as to selectvehicles or parts for vehicles based on sound. Vehicle sounds are notarbitrary but a product of design and function and therefore can beassociated with particular vehicles. For example, motorcycle “pipes” canbe exchanged to provide a particular sound profile, or a street racingcar muffler may be selected to increase or decrease noise made duringdriving.

In this regard, databases can be built of vehicle sounds. The databasescan aggregate recorded vehicle sounds. The recorded vehicle sounds canbe processed to remove non-vehicle noise and standardize audio to aid incomparison and analysis. Different portions or types of recorded audiocan also be identified, such as idling sound, acceleration sound, anddeceleration sound. In embodiments, sounds inaudible to the driver orspectators or vibrations may be captured by recording devices moresensitive than the human ear. Collected audio can be stored andanalyzed. In embodiments, a cellular phone gyroscope, accelerometer, orsimilar device can be used to capture vibrations or movements of avehicle which can be analyzed separately, or be used to process orcombined with the audio recording.

A vehicle can be identified and associated with its sound or sounds.Deviations to these sounds can be accounted for based on particularmodifications, operating conditions (vehicular or environmental), etcetera. For example, an aftermarket muffler, a particular tire tread, afailing belt, a gravel road, improper ignition timing, worn shocks, orrain can modify the aural signature of a particular vehicle. Theseauditory components can be identified in the analysis to more accuratelyidentify the vehicle and its current status regardless of environment.

The database can be refined through learning algorithms or user inputregarding recorded audio. In various embodiments, audio is submittedwith information about a vehicle by a recorder. Subsequent audio can beanalyzed and a closest match identified, which can then be updated bymachine learning or a user to expand and improve the database. Inembodiments, a closest match cannot be identified and a new entity canbe created in the database.

The vehicle sound database can be leveraged for a variety of uses. In anembodiment, service providers offering products relating to vehicles canpre-populate vehicle information based on identifying the audio heard.For example, an insurance provider can “listen” to a motorcycle andguess the vehicle to be insured, populating the insurance applicationwith information regarding the vehicle identified to ease and expeditethe application process. In another example, an auto parts seller cananalyze audio of a race car and provide a product filter or list matchedto the race car.

In additional embodiments, audio of a vehicle can be used to verifydetails provided as to its model, modifications, or condition. Forexample, if a lender or insurer needs verification that a vehicleremains in stock condition and has been properly maintained, analysis ofan audio signal is more efficient than inspecting the vehicle. Further,interest or insurance rates can be modified based on the vehiclecondition or modifications. For example, the use of premium tires,especially in inclement weather, or consistent maintenance may increaseconfidence in the owner, thereby reducing associated risk and ratesbased thereon. Further, audio of vehicles can be used to track vehicleperformance, detecting aspects such as rapid acceleration or losses ofwheel traction. Behavior inferred from such audio may also be used bylenders, insurers, or other interested parties. Behavior-based insurancehardware, software, and techniques can integrate with or utilize sensordata or other information herein to aid in insurance rate calculations.Monitoring for insurance or other purposes can be continuous orepisodic.

In additional embodiments, diagnostics or tuning can be improved usingvehicle audio. By listening to a vehicle and discovering deviations,problems can be identified. These diagnostics via audio analysis can beused by mechanics, owners, or others to determine condition or repairvehicles.

Entertainment can also be realized through the vehicle audio database.The vehicle audio database can permit users to test their knowledge ofvehicle audio by playing vehicles back and allowing them to guess, orallow submission of new or modified vehicles unknown to the database andallow others to guess as to the identity.

A variety of other embodiments are also possible. Parties shopping forvehicle parts can be provided example sounds representing the changes tothe vehicle's aural signature once the modifications are complete, orparties shopping for vehicles can more generally hear the vehicle'saural signature with or without modification. These can be shown on ascreen, in a holograph, in virtual reality, et cetera. Car censuses canbe conducted which identify passing vehicles based on a static or mobilemicrophone to determine more accurate traffic numbers relating to thenumber of vehicles and their individual types and condition. Vehicularand pedestrian traffic can be analyzed. Individual vehicles can betracked based on their “acoustic fingerprint” using distributed ormobile listening devices to assist law enforcement or intelligence withtracking a vehicle without following in chase.

Car-specific advertising can be provided on dynamic advertisement usingdirectional microphones to detect vehicle types in advance and provideadvertising content keyed to the vehicle (e.g., a luxury car receivesdifferent advertising than an economy car). In embodiments,“micro-auctions” can be conducted for advertisers, displaying thehighest bid ad, based on audio data (and/or other sensor data, such asvisual data) for individual vehicles or aggregated vehicle data atparticular times and places. Instantaneous/real-time ad time, or futuread time based on statistics, could accordingly be sold or auctioned. Thead pricing can be based on time of day, exposure time per vehicle,vehicle speed(s), vehicle position, vehicle angle of approach, etcetera. Specific audiences can be identified, with ad(s) displayed forthe specific audience (down to an individual vehicle) transitioning todifferent advertisements when the specific audience is beyond billboardview or if they are outbid. In an embodiment, a holographic ormulti-angle billboard could be employed to target individual vehicles orgroups of vehicles based on their relative position. The multi-anglebillboard can be employed using a variety of lenses and mirrors, whichcan be static or movable, to provide multiple ads per billboard based onviewing location.

Noise cancelling features built into vehicles can be made more accuratebased on the known aural signature(s) associated with the vehicle.Alternatively, noise generation can be provided for a designer vehiclewhere a specific vehicle noise is sought but not created by the vehicleitself (e.g., vehicle runs quiet but can broadcast loud exhaust noiseselectively). In addition, a vehicle sound database can be leveraged tosimulate vehicle rides or immersive experiences (alone or in combinationwith other data such as that collected by a gyroscope, accelerometer, orother sensors in a vehicle), or improve the accuracy and quality ofmovies or other replayable media involving vehicles.

In embodiments, user vehicles or devices can participate in developmentof the database. For example, vehicle sensors or user mobile devices canprovide audio infatuation, location information, diagnostic information,et cetera. This information can be associated with vehicles andcollected audio data in the vehicle database. Based on this information,more accurate details as to the vehicle can be developed, and algorithmsusing machine learning or other techniques to aid in identifyingvehicles and their conditions can be improved by using multipledatapoints the same point in time for one vehicle.

Aspects herein can be accomplished with an application on a mobilephone, tablet, computer, or other device. A recording module can use amicrophone or other audio recording component to collect audio data. Ananalysis module can analyze the audio data to determine characteristicsfor identifying a vehicle or components thereof. The database is used tostore audio data for analysis and comparison, and may be updated withnew audio data as it is provided. A processing module may also beprovided to process recorded audio for noise reduction, standardization,and so forth. In embodiments, one or more modules can be located remoteto the user's device, establishing a client server relationship. Forexample, the database and some or all of the analysis module may belocated on third party servers to provide storage for large amounts ofaudio data as well as increase processing power available for analysis.

It is to be appreciated the subject invention is described below morefully with reference to the accompanying drawings, in which illustratedembodiments of the present invention are shown. The present invention isnot limited in any way to the illustrated embodiments as the illustratedembodiments described below are merely example of the invention, whichcan be embodied in various forms, as appreciated by one skilled in theart. Therefore, it is to be understood that any structural andfunctional details disclosed herein are not to be interpreted aslimiting, but merely as a basis for the claims and as a representativefor teaching one skilled in the art to variously employ the presentinvention. Furthermore, the terms and phrases used herein are notintended to be limiting but rather to provide an understandabledescription of the invention.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present invention, examplemethods and materials are now described.

It must be noted that as used herein and in the appended claims, thesingular forms “a,” “an,” and “the” include plural referents unless thecontext clearly dictates otherwise. Thus, for example, reference to “astimulus” includes a plurality of such stimuli and reference to “thesignal” includes reference to one or more signals and equivalentsthereof as known to those skilled in the art, and so forth.

It is to be appreciated that certain embodiments of this invention asdiscussed below are a software algorithm, program or code residing oncomputer useable medium having control logic for enabling execution on amachine having a computer processor. The machine typically includesmemory storage configured to provide output from execution of thecomputer algorithm or program. As used herein, the term “software” ismeant to be synonymous with any code or program that can be in aprocessor of a host computer, regardless of whether the implementationis in hardware, firmware or as a software computer product available ona disc, a memory storage device, or for download from a remote machine.The embodiments described herein include such software to implement theequations, relationships and algorithms described above. One skilled inthe art will appreciate further features and advantages of the inventionbased on the above-described embodiments. Accordingly, the invention isnot to be limited by what has been particularly shown and described,except as indicated by the appended claims. Further, although at leastone series of steps are presented as an example method of practicing oneor more embodiments described herein, it will be appreciated by thoseskilled in the art that the steps identified may be practiced in anyorder that is practicable, including without limitation the omission ofone or more steps.

Nonetheless, aspects herein which may be implemented in software neednot be, and can be realized according to non-software techniques unlessexplicitly described otherwise.

Turning to the drawings, FIGS. 1-3, depict example methodologies 100,200, and 300 for utilizing one or more embodiments described herein.FIG. 5 illustrates an example block diagram of a system 500 forimplementing one or more aspects herein. A computing device 400, such asthat shown in FIG. 4, may interface with one or more computing devicesthat include data identifying one or more accounts (or users, vehicles,et cetera) and/or comprising data collected or analyzed in relationthereto. Such data may be received by an interface device 404 and may bedisplayed through an output device connected to and interface device404. Such data may be stored in a memory device 410. Alternatively, suchdata may be stored elsewhere, such as in a network cloud, and sent to orretrieved by computing device 400 to execute the processes describedherein.

Specifically, computing device 400, components thereof, or alternativeelectrical or electronic components may be employed to collect, send,and/or receive data in accordance with aspects herein. Such componentscan include mobile phones or other mobile devices, apps installedthereto, and sensors (e.g., location-sensing hardware, gyroscopes,accelerometers) aboard or leveraged by such mobile devices or apps. Dataaggregated from one or more points can be transferred over networks toassist with analyzing audio data. Internet of Things (IoT) devices, orsensors or components compatible with IoT technologies can also beemployed with aspects herein alone or in combination with audiocollection devices utilized by a user. In embodiments, sensors (e.g.,microphones, gyroscopes, accelerometers) aboard vehicles (e.g., withinthe passenger compartment, on the engine, elsewhere on a vehicle) can beused to collect sensor data. Other sensors external to processing orcommunication devices can also be employed. Further, an app stored inmemory or employed in a distributed fashion may be employed forcollecting and submitting audio for analysis.

Also referring to FIGS. 1 to 3, example methods of operating computingdevice 400 will be described herein. However, aspects of these methods,or the methods in their entirety, may be performed without the use ofcomputing device 400 or similar devices.

Referring specifically to FIG. 1, the flow chart illustrates an examplemethodology for matching vehicle sounds and maintaining a database ofvehicle sounds for matching. Methodology 100 begins at 102 and proceedsto 104 where audio associated with a vehicle is detected. This can beusing a static microphone, a movable microphone part of a mobile deviceor computer, microphones aboard the vehicle capable of interacting withexternal systems, and so forth. Detection can be automatic based oncontinuous monitoring or on demand based on automatic or manual action.In an embodiment, a user may use a control on a mobile app to beginrecording or transmit audio.

At 106, methodology 106 processes and filters the audio. This aspect maynot be included in all embodiments, but can be provided to reduce noise,standardize audio format, normalize the audio data, and so forth, whichprovides for standardization in the database and improve analysisconsistency.

At 108, the audio received is compared to audio in the database. Thiscan be done over the entire audio data or using specific points of audiodata represented according to parameters or waveform. Aspects such aspeaks and troughs of the waveform, the presence and amplitude ofparticular frequencies, overall frequency or period length, and others,can be compared. Transformations or augmentations associated withmodifications or particular conditions can be applied to determinewhether a modified (or de-modified) version is a closer match than audiocomparisons for a stock vehicle or average condition.

Based on this comparison and analysis, at 110 a closest match isidentified. In an alternative embodiment, no closest match isidentified, and a new entry can be created in the database.

In methodology 100, the closest match is then provided to the submittinguser for review. At 114 confirmation of its correctness or updates (tomodel, modifications, et cetera) can be provided to properly match thevehicle to the provided audio data. If such updates are provided, at 116the database is updated to include the new audio for future matching.Alternatively the audio could be associated or merged with dataregarding similar or identical vehicles to improve a model forcomparison and matching. For particular variants or vehicles with“mods,” further processing can occur to improve the machine learning andanalysis conducted through interpolation of distinct models or variantsbased on the sound and information provided. Thereafter, methodology 100ends at 118.

FIG. 2 illustrates a flow chart for an example methodology 200 for usingvehicle audio to pre-populate information related thereto. Methodology200 begins at 202 and proceeds to 204 where vehicle sound is detected orprovided. In embodiments, pre-recorded sound can be provided in additionto sound which is live or recorded on demand using a mobile orpre-positioned device. The device may include local processingcapability, components for transmitting the sound or data representativethereof over wired or wireless means, or combinations thereof. At 206the sound may be processed and filtered to reduce or remove noise,amplify or suppress particular frequencies or aspects, normalize thegain or amplitude, standardize the format, et cetera. Thereafter, at208, the (raw or unprepared) audio may be compared or analyzed in viewof known audio. At 210, the closest match is identified. Based on thisclosest match, vehicle information can be pre-populated into variousforms or systems for use or review by the insurer, the insured, or otherparties. Thereafter, at 214, methodology 200 ends.

FIG. 3 illustrates a flow chart for an example methodology 300determining conditions associated with vehicle operation. Methodology300 begins at 302 and proceeds to 304 where audio of a vehicle isdetected. At 306, the audio may be processed or filtered in preparationfor transmission, storage, or analysis. Thereafter, at 308, the audio isanalyzed and/or compared to known audio in the database, with a closestmatch identified at 310.

Based on the closest match, at 312, deviations are identified. Thedeviations can then be identified to determine vehicle conditions.Vehicle conditions can include modifications to the vehicle, vehicleoperating condition (e.g., engine trouble, tire tread), environmentaloperating condition (e.g., road surface, weather), and so forth. Theconditions identified can provide further information about the vehicle,its use, or the driver.

As will be appreciated, methodologies 100, 200, and 300, andalternatives described, can be combined in various fashions to effectestablishment, maintenance, and use of an audio database and analysis ofaudio leveraging such.

Referring to FIG. 4, illustrated therein is an example embodiment of acomputing device as might be used when utilizing the systems and methodsdescribed herein. In one embodiment, computing device 400 includesmemory 410, a processor 402, an interface device 404 (e.g., mouse,keyboard, monitor), a network device 406. Memory 410 in one examplecomprises a computer-readable signal-bearing medium. One example of acomputer-readable signal-bearing medium comprises a recordable datastorage medium, such as a magnetic, optical, biological, and/or atomicdata storage medium. In another example, a computer-readablesignal-bearing medium comprises a modulated carrier signal transmittedover a network coupled with a system, for instance, a telephone network,a local area network (“LAN”), the Internet, and/or a wireless network.In one example, memory 410 includes a series of computer instructionswritten in or implemented with any of a number of programming languages,as will be appreciated by those skilled in the art.

Memory 410 in one example includes RAM 412, hard drive 415, which mayinclude database 416. Database 416 in one example holds information,such as information that relates to users and/or parties interactingwith a system.

FIG. 5 illustrates an example system 500 for implementing aspectsdisclosed herein. FIG. 5 includes vehicle audio system 510 and managedsensors 592 for sensing, e.g., audio data related to sensed entities594.

Managed sensors 592 can include various sensors or collectors forsensing aspects relating to vehicles, pedestrian traffic, and otherthings. Managed sensors 592 can be part of vehicle audio system 510,separate from but managed by vehicle audio system 510, or third partycollectors to which vehicle audio system 510 has at least partialaccess. Managed sensors 592 can include one or more microphones.

Microphones can be directional and provide rich audio informationaccompanied by metadata relating to direction and other non-audiocharacteristics which can be used in analysis of the audio. Inembodiments corroborating sensors can be used to provide additionalmetadata accompanying the audio. Corroborating sensors can include othermicrophones, cameras, magnetic field sensors, radar, sonar, lasers, etcetera. Corroborating sensors can be located at the same location as amicrophone (or other sensor) or other locations.

Sensed entities 594 can include, but are not limited to, vehicles.Sensed entities can be sensed based on sound created or emitted. Inembodiments sensed entities are sensed according to othercharacteristics, profiles, or techniques.

Sensed entities 594 can include or be associated with various sharedsensors 596. Shared sensors 596 can provide additional sensor data tovehicle audio system 510 to create or supplement data in vehicle audiodatabase 540. Shared sensors 596 can include, but are not limited to,user or drive mobile devices, Internet-of-Things (IoT) devices, andsensors aboard the car itself. Additional data provided can include, butis not limited to, additional audio from different locations orperspectives, vehicle diagnostics, location or movement data, et cetera.

Vehicle audio system 510 receives data from managed sensors 592 and/orshared sensors 596 via communication modules 520. Received data isprovided to analysis module 530 for analysis. Communication module 520can provide notifications or data to a variety of parties, such asinsurers, police, emergency services, vehicle enthusiasts or hobbyists,drivers or owners of vehicles, advertisers, and others. In embodiments,communication module 520 can also transmit stored audio data to devicesor systems which simulate real-world vehicle sounds to emulate the noiseof a particular vehicle or configuration.

Analysis module 530 can include a number of subcomponents or modules foranalyzing received audio data, and, in embodiments, accompanyingmetadata.

In an embodiment, a comparator module is included in analysis module 530for comparing received audio and metadata with stored audio andmetadata. In embodiments, statistical analyses can be performed by thecomparator module. Different categories of data can be used whencomparing received data and stored data. One category can be vehicledata for comparing particular makes and models of vehicles. Anothercategory can include vehicle component data for comparing particularstock or aftermarket parts for vehicles. Another category can includecondition data, which can include data related to vehicle condition(e.g., damage) and/or environmental conditions (e.g., temperature orhumidity) which can be assessed to perform diagnostics, correct oraugment received audio, or both.

In embodiments, an audio correction module can be included in analysismodule 530. The audio correction module can perform various actions onreceived audio, such as filtering or squelching noise or other sounds,changing audio levels, normalizing audio, et cetera. In this fashion,audio can be prepared for more accurate comparison.

In embodiments, a diagnostic module can be included in analysis module530. Diagnostic module can utilize results from other analysis (e.g.,vehicle and condition comparisons) to diagnose a vehicle issue (e.g.,failing belt, tire with slow air leak). Diagnostics can compare storeddata previously developed for the same vehicle to determine changes overtime (e.g., a slow leaking tire that gradually begins to exhibit soundsassociated with a flat). In an embodiment, the diagnostic module caninteract with communication module 520 to notify a vehicle owner oroperator, or a third party, regarding the issue.

In embodiments, one or more of a statistical component or a machinelearning component can be included in analysis module 530. A statisticalcomponent can perform statistical modeling to predict, interpret,interpolate, solve, or otherwise determine vehicle audio trends,changes, conditions, or identities in the presence of varying parameters(e.g., direction, distance, noise, temperature, humidity, pressure, etcetera). A machine learning component can utilize machine learning todiscover and train systems to compare and determine vehicle identitiesor conditions in the presence of varying parameters.

Vehicle audio system 510 can also include computing components 550,which can include hardware and/or software, including aspects of system400 discussed herein, and/or other software elements to provide acomputing environment and computing architectural support for theaspects described herein.

The terms “engine” and “module” denote a functional operation that maybe embodied either as a stand-alone component or as an integratedconfiguration of a plurality of subordinate components. Thus, enginesand modules may be implemented as a single engine/module or as aplurality of engine/modules that operate in cooperation with oneanother. Moreover, engines/modules may be implemented as softwareinstructions in memory 410 or separately in any of hardware (e.g.,electronic circuitry), firmware, software, or a combination thereof. Inone embodiment, engines/modules contain instructions for controllingprocessor 402 to execute the methods described herein. Examples of thesemethods are explained in further detail herein.

The techniques described herein are example, and should not be construedas implying any particular limitation on the present disclosure. Itshould be understood that various alternatives, combinations andmodifications could be devised by those skilled in the art. For example,steps associated with the processes described herein can be performed inany order, unless otherwise specified or dictated by the stepsthemselves. The present disclosure is intended to embrace all suchalternatives, modifications and variances that fall within the scope ofthe appended claims.

The terms “comprises” or “comprising” are to be interpreted asspecifying the presence of the stated features, integers, steps orcomponents, but not precluding the presence of one or more otherfeatures, integers, steps or components or groups thereof.

Although the systems and methods of the subject invention have beendescribed with respect to the embodiments disclosed above, those skilledin the art will readily appreciate that changes and modifications may bemade thereto without departing from the spirit and scope of the subjectinvention.

What is claimed is:
 1. A system, comprising: a vehicle audio databaseconfigured to store vehicle audio segments and metadata associated withthe vehicle audio segments; a communication module for receiving newaudio segments and new metadata from a plurality of sensors, wherein theplurality of sensors includes an audio sensor for collecting the newaudio segments and a corroborating sensor for collecting the metadata; acorrection module configured to augment the new audio segment based on ametadata parameter; an analysis module configured to analyze the newaudio segments, wherein analysis of the new audio segments is based onaugmentation by the correction module; and a comparator moduleconfigured to compare the new audio segment with the vehicle audiosegments of the vehicle audio database, wherein comparison determines aclosest match and deviations between the new audio segment and theclosest match, and wherein the analysis module determines a conditionassociated with the deviations.
 2. The system of claim 1, furthercomprising the plurality of sensors for collecting the new audiosegments or the new metadata.
 3. The system of claim 2, wherein theaudio sensor of the plurality of sensors includes a microphone.
 4. Thesystem of claim 2, wherein the corroborating sensor of the plurality ofsensors includes one or more of a camera, a magnetic field sensor, aradar, or a sonar.
 5. The system of claim 2, wherein at least one of theplurality of sensors is a shared sensor.
 6. The system of claim 5,wherein the shared sensor is one of a mobile device of a driver, anInternet-of-Things device associated with a vehicle, or an onboardvehicle sensor of a vehicle.
 7. The system of claim 1, wherein thecommunication module is configured to communicate with at least one ofemergency services, a vehicle owner, a vehicle operator, an insurer, anadvertiser, or a third party.
 8. The system of claim 1, furthercomprising a machine learning module of the analysis module configuredto determine an identity or condition based on varying parametersbetween the new metadata and the metadata.
 9. The system of claim 1,wherein the comparator module utilizes categories of data including avehicle make and model category, a vehicle parts category, and acondition category.
 10. The system of claim 1, wherein the condition isan environmental condition in which the vehicle is operating.
 11. Amethod, comprising: storing, by a vehicle audio database, vehicle audiosegments and metadata associated with the vehicle audio segments;receiving, by a communication module, new audio segments and newmetadata from a plurality of sensors, wherein the plurality of sensorsincludes an audio sensor for collecting the new audio segments and acorroborating sensor for collecting the metadata; augmenting, by acorrection module configured, the new audio segment based on a metadataparameter; analyzing, by an analysis module configured, the new audiosegments, wherein analysis of the new audio segments is based onaugmentation by the correction module; and comparing, by a comparatormodule configured, the new audio segment with the vehicle audio segmentsof the vehicle audio database, wherein comparison determines a closestmatch and deviations between the new audio segment and the closestmatch, and determining, by the analysis module, a condition associatedwith the deviations.
 12. The method of claim 11, wherein the conditionis an environmental condition in which the vehicle is operating.
 13. Themethod of claim 11, wherein the corroborating sensor of the plurality ofsensors includes one or more of a camera, a magnetic field sensor, aradar, or a sonar.
 14. A non-transitory computer readable medium storinginstructions, that when executed, cause: storing, by a vehicle audiodatabase, vehicle audio segments and metadata associated with thevehicle audio segments; receiving, by a communication module, new audiosegments and new metadata from a plurality of sensors, wherein theplurality of sensors includes an audio sensor for collecting the newaudio segments and a corroborating sensor for collecting the metadata;augmenting, by a correction module configured, the new audio segmentbased on a metadata parameter; analyzing, by an analysis moduleconfigured, the new audio segments, wherein analysis of the new audiosegments is based on augmentation by the correction module; andcomparing, by a comparator module configured, the new audio segment withthe vehicle audio segments of the vehicle audio database, whereincomparison determines a closest match and deviations between the newaudio segment and the closest match, and determining, by the analysismodule, a condition associated with the deviations.
 15. Thenon-transitory computer readable medium of claim 14, wherein thecomparator module utilizes categories of data including a vehicle makeand model category, a vehicle parts category, and a condition category.16. The non-transitory computer readable medium of claim 14, wherein thecommunication module is configured to communicate with at least one ofemergency services, a vehicle owner, a vehicle operator, an insurer, anadvertiser, or a third party.
 17. A device, comprising: one or moreprocessors; and instructions that, when executed by the one or moreprocessors, cause the device to: store, by a vehicle audio database,vehicle audio segments and metadata associated with the vehicle audiosegments; receive, by a communication module, new audio segments and newmetadata from a plurality of sensors, wherein the plurality of sensorsincludes an audio sensor for collecting the new audio segments and acorroborating sensor for collecting the metadata; augment, by acorrection module configured, the new audio segment based on a metadataparameter; analyze, by an analysis module configured, the new audiosegments, wherein analysis of the new audio segments is based onaugmentation by the correction module; and compare, by a comparatormodule configured, the new audio segment with the vehicle audio segmentsof the vehicle audio database, wherein comparison determines a closestmatch and deviations between the new audio segment and the closestmatch, and determine, by the analysis module, a condition associatedwith the deviations.
 18. The device of claim 17, wherein at least one ofthe plurality of sensors is a shared sensor, and wherein the sharedsensor is one of a mobile device of a driver, an Internet-of-Thingsdevice associated with a vehicle, or an onboard vehicle sensor of avehicle.
 19. The device of claim 18, wherein the instructions furthercause the device to determine, by a machine learning module of theanalysis module, an identity or condition based on varying parametersbetween the new metadata and the metadata.